In general, group supervisory control is effected in a system in which a plurality of elevators are operating. In such a system, a variety of types of controls are performed including a car assignment control in which an optimally assigned car is selected in response to a hall call occurring at a certain hall, a deadhead or forwarding operation in which some cars are controlled to travel to a specified floor or floors independently of the occurrence of a hall call particularly in peak periods, division of service zones, etc. Recently, various methods have been proposed for predicting the results of group supervisory control, i.e., group supervisory control performance such as waiting times and the like, and accordingly setting control parameters, as disclosed in the Japanese Patent No. 2664766, Japanese Patent Application Laid-Open No. Hei 7-61723, etc,
In the above-mentioned two prior art references, there are described systems which, using a neural net which receives, as its inputs, traffic demand parameters and evaluating operation parameters for call assignment and generates, as its output, a group supervisory control performance, evaluates the output result of the neural net to set optimal evaluating operation parameter accordingly.
However, with the above-mentioned prior art references, what is set based on the results of the group supervisory control performance prediction is limited to a single evaluation operation parameter for the call assignment, and hence there is a limitation to improvements in transportation performance due solely to the operation or calculation which uses such a single evaluation operation parameter for the call assignment. That is, it is necessary to use a variety of rules such as deadhead, zone division, etc., depending upon traffic conditions, and thus no really excellent group supervisory control performance could be obtained.
Moreover, though a neutral net has merit in that its operation of calculation accuracy can be improved through learning, it has a demerit in that it takes much time until it reaches a practical level of operation or calculation accuracy.
In the systems disclosed in the above-mentioned prior art references, it is impossible to obtain an expected level of group supervisory control performance unless the neural net has been subjected to learning in advance at the factory. In addition, the group supervisory control performance prediction accuracy by the neural net decreases greatly when traffic demand changes rapidly due to a change in the tenants in a building.
Moreover, a method of calculating a group supervisory control performance value or gain at a constant traffic demand by means of probability operations is described in a teaching material of Japan Society of Mechanical Engineers 517th Meeting, entitled "Theory and Practical State of Elevator Group Supervisory Control Systems". According to this method, however, only an average value of waiting times is calculated for instance, and other group supervisory control performance indices such as the maximum value and distribution of waiting times, the number of non-stop passages of fully loaded cars, the number of cars which left off passengers, etc. cannot be calculated. Therefore, it is impossible to change control parameters while referring to predicted values of various group supervisory control performance indices.
Further, when a group supervisory control system is developed, a group supervisory control simulation is usually carried out to understand its performance. In such a group supervisory control simulation, individual passenger data are input, and the same control operations as those performed in the product are done for each hall call made by a passenger, thereby allocating a car to the call. In general, car behaviors are imitated on the computer according to the call assignment, whereby the performance of the system, i.e., the group supervisory control performance, is output. Since the same control operations as those in this simulation product can be done in principle, the prediction accuracy of the group supervisory control performance is very high.
Ideally, it is desired that the group supervisory control simulation used in this product development process be built into a group supervisory control system without any change, and the group supervisory control performance of the system be predicted through simulations to thereby determine an optimal control method. If this could be achieved, the problems in the method of using the neural net and the probability operations as referred to above would be solved.
However, this means that the same operations are carried out a plurality of times while the actual group supervisory control is being effected. Therefore, it is realistically difficult to complete the simulation within real time by means of a microcomputer generally used in an actual group supervisory control system. That is, a method is sought by which it is possible to complete operations or calculations within real time to predict the group supervisory control performance with high accuracy.
The present invention is intended to solve the above-mentioned problems in the prior art, and provide an elevator group supervisory control system which provides a real time simulation during group supervisory control, select an optimal rule set at all times, and perform excellent group supervisory control.